In subsequent works, Tzeng et al. [1]} and Long et al. [2]} involved also deep cnn, extending mmd. In particular, the authors of [1]} introduced the Deep Domain Confusion network (DDC), comprising two cnns for the source and the target domains with shared weights; DDC is optimized in the source domain using a classification loss, while the domain difference is assessed by an adaptation layer exploiting the mmd metric.
Since DDC adapts only one layer of the network, limiting the transferability of multiple layers, rather than using a single layer and a linear mmd, the authors in [2]} introduced a Deep Adaptation Network (DAN), aiming at matching the shift across domains in marginal distributions, by adding multiple adaptation layers and exploring multiple kernels. However, since, in practice, the source classifier cannot be directly used in the target domain, Long et al. [5]} proposed a joint adaptation network (JAN) to align the shift in the joint distributions of input features and output labels in multiple domain-specific layers based on a Joint mmd (JMMD) criterion. Another remarkable work is [6]}, where the authors introduced a weighted mmd model characterized by an extra weight for each class belonging to the source domain when the target domain class weights are not the same as those in the source domain.
